Huggingface trainer use gpu amp for PyTorch. The API supports distributed training on multiple GPUs/TPUs, Hello, I am trying to incorporate knowledge distillation loss into the Seq2SeqTrainer. , I am getting same speed. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional methods outlined in the multi-GPU section. Find the 🤗 Accelerate example further down in this guide. The Trainer automatically manages multiple machines, and this can speed up training tremendously. However, the Accelerator fails to work properly. Modified 2 years, 9 months ago. Viewed 4k times Part of NLP Collective 7 . It takes ~3 sec to process 128 samples (16 per each GPU). Basics for Multi GPU Training with Huggingface Trainer. . I would like it to use a GPU device inside a Colab Notebook but I am not able to do it. torchrun --nproc_per_node=2 trainer-program. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex I have multiple GPUs available in my enviroment, but I am just trying to train on one GPU. cuda. Related topics Topic Replies Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex . Even when I set use_kd_loss to False (the loss is computed by the super call only), it still does not To disable this warning, please explicitly set TOKENIZERS_PARALLELISM=(true | false) build trainer with on device: cuda:1 with n gpus: 1 build trainer with on device: cuda:2 with n gpus: 1 build trainer with on device: cuda:3 with n gpus: 1 build trainer with on device: cuda:0 with n gpus: 1 finished in 0:04:15. When I use Trainer module, I am getting faster processing only in one GPU. Although, DDP does seem to be faster than PP (less time for the same number of steps). Why is that? Hi, I’ve set CUDA_VISIBLE_DEVICES=0,1,2,3 and torch. 2 Likes. but it didn’t worked Is there any configuration to use the GPU with the Trainer API? If I use the native version of the PyTorch pretrain tutorial example, the GPU is used correctly. This is the same for GPUs 1 and 2. That page says “If you have access to a machine with multiple GPUs, try to run the code there. What is the reason of it using CPU instead of GPU? Thanks for the clear issue and resolution - very helpful in getting DDP to work. The GPU space is enough, however, the training process only runs on CPU instead of GPU. Then, i found that we could put devices_ids directly to nn. I would appreciate your idea. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex When i use model. @younesbelkada, I noticed that using DDP (for this case) seems to take up more VRAM (more easily runs into CUDA OOM) than running with PP (just setting device_map='auto'). ; your model can compute the loss if a labels argument is provided and that loss is returned as the first element of the tuple (if your model The Trainer will work out of the box on multiple GPUs or TPUs and provides lots of options, like mixed-precision training (use fp16 = True in your training arguments). This extension can be implemented by setting the environment variable CUDA_VISIBLE_DEVICES appropriately before the training process begins. But when I run my Trainer, nvtop shows that only GPU 0 is computing anything. 937331 seconds finished in 0:04: The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. from_pretrained(“”). It would be helpful to extend the train method of the Trainer class with additional parameters to specify the GPUs devices we want to use during training. ” I’m working on a machine with 8 Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. Trainer goes hand-in-hand with the TrainingArguments class, which offers a wide range of options to customize how a model is trained. I am using accelarteor to train a model on multiple GTX 1080 GPU. amp instead of apex. Each method can improve speed or memory usage which is My server has two GPUs,(index 0, index 1) and I want to train my model with GPU index 1. The Trainer class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for NVIDIA GPUs, AMD GPUs, and torch. How to run huggingface Helsinki-NLP models. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex Why is it that when I use Trainer, multiple GPUs are used for training, but only one GPU is used for evaluation? When I compared the GPU usage for training and evaluation, I found that: only the memory of GPU-0 is increased, and only its GPU-util is not 0. ? Many thanks I’m going through the huggingface tutorials and going through the “Training a causal language model from scratch” sections. The Trainer class can auto detect if there are multiple GPUs. And causing the evaluation to be slow. Would you please help me how you use multiple GPU for fine tunning the model. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. I have a VM with 2 V100s and I am training gpt2-like models (same architecture, fewer layers) using the really nice Trainer API from Huggingface. 0: 2554: June 14, 2023 Trainer API for Model Parallelism on Multiple GPUs. Trainer class using pytorch will automatically use the cuda (GPU) version without any additional specification. I’m training environment is the one-machine-multiple-gpu setup. py file: import os from tokenizers import ByteLevelBPETokenizer from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer According to what I’ve read (HuggingFace doc), deepspeed automatically identifies the GPUs and as I have stage 2 zero optimisation (see config below) the memory used in training of each gpu should be lower than if using huggingface Trainer with distributed data parallel. transformers. I use the trainer in hugging face which I understand it will use multiple GPu . Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. Together, these two Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. my code is: model = AutoModel. This is generally achieved by utilizing the GPU as much as possible and thus filling GPU memory to its limit. distributed, torchX, torchrun, Ray Train, PTL etc) or can the HF Trainer alone use multiple GPUs without being launched by a third-party distributed launcher? Why, using Huggingface Trainer, single GPU training is faster than 2 GPUs? Ask Question Asked 2 years, 9 months ago. We will go over everything it supports in Chapter 10. Shouldn’t it be at 100% consistently until the training it complete? Here is my train. When using it on your own model, make sure: your model always return tuples or subclasses of ModelOutput. amp because amp was not part of my apex installation. Even using A100 GPU. This causes per_device_eval_batch_size to be only 1 or it goes OOM. While it is advised to max out GPU usage as much All but one GPU are idle at any given moment: if 4 GPUs are used, it’s nearly identical to quadrupling the amount of memory of a single GPU, and ignoring the rest of the hardware. 2. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. I would expect all 4 GPU usage bars in the following screenshot to be all the way up, but devices 1-3 show 0% usage: I hi All, would you please give me some idea how I can run the attached code with multiple GPUs, with define number of 1,2? As I understand the trainer in HF always goes with gpu:0, but I need to specify the number of GPUs like 1,2. from_pretrained('bert-base-uncased') model = BertForNextSentencePrediction. 1: ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" from transformers import Trainer If you’re using Jupyter, you can also use magic commands (again, at the top, before importing anything else): Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. This extension can be When training on multiple GPUs, you can specify the number of GPUs to use and in what order. My current machine has 8 gpu cards and I only want to use some of them. device_count() . This is my proposal: tokenizer = BertTokenizer. Motivation. Do I need to launch HF with a torch launcher (torch. Greater flexibility in specifying In the above example, your effective batch size becomes 4. You just need to copy your code to Kaggle, and enable the I had the same issue - to answer this question, if pytorch + cuda is installed, an e. DataParallel(model, devices_ids[0,1,2]). The training script that I use is similar to the run_summarization script. I although I have 4x Nvidia T4 In the above example, your effective batch size becomes 4. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for large models and how they are integrated in the Trainer and 🤗 Accelerate. When i put only one GPU, the training goes on it, but as soon as i put 2 or 3, the training is done on the fi The Transformers Trainer is only using 1 out of 4 possible GPUs. I’m training my own prompt-tuning model using transformers package. 🤗Transformers. Training New Hi! I am pretty new to Hugging Face and I am struggling with next sentence prediction model. Alternatively, use 🤗 Accelerate to gain full control over the training loop. but my results are very strange and very different than when I use 1 GPU. train() on my Trainer and it begins training, my GPU usage fluctuates from 0% to around 55%. to(“cuda”) training_args = TrainingArguments Trainer. But then the device is After reading the documentation about the trainer https://huggingface. It’s used in most of the example scripts. It works for cpu and 1 gpu but freezes when I try run on multiple GPUs (stuck at the first batch). I’ve read the Trainer and TrainingArguments documents, and I’ve tried the CUDA_VISIBLE_DEVICES thing already. to("cuda:0"), the GPU with id 0 has 100% consommation and memory usage. @philschmid @nielsr your help would be appreciated import os import torch import pandas as pd from datasets import load_dataset The Transformers Trainer is only using 1 out of 4 possible GPUs. py All but one GPU are idle at any given moment: if 4 GPUs are used, it’s nearly identical to quadrupling the amount of memory of a single GPU, and ignoring the rest of the hardware. 8. sh as per your server. Change specifications in script. 5: if it matters, I modified trainer. This can be useful for instance when you have GPUs with different computing power and want How can i use SFTTrainer to leverage all GPUs automatically? If I add device_map=“auto” I get a Cuda out of memory exception. I tried to use cuda and jit from numba like this example to add function decorators, but it still doesn’t help. co/docs/transformers/main_classes/trainer#pytorch-fully-sharded-data-parallel and further on the sorry I am trying to fine tune got-neo because of the Cuda memory issue I need to use multiple GPU. understanding gpu usage huggingface classification - Total optimization steps. What can be the source of these differences ? When I run . But when I run my Trainer, nvtop shows that only GPU 0 is c I am using the transformer’s trainer API to train a BART model on server. device_count() shows 4. py Order of GPUs. 2: 2014: October 18, 2023 Why is Trainer only using 1 (not 4) GPUs? Beginners. When training large models, there are two aspects that should be considered at the same time: Maximizing the throughput (samples/second) leads to lower training cost. 3. Now, to select which GPUs to use and their order, Using 3 GPUs for training with Trainer() of transformers. from_pretrained('bert-base-uncased', return_dict=True) @aclifton314 Hi, sorry I am trying to train and evaluate my GPT-2 by applying the trainer with GPU ,I am not sure how I can pass my model and the training data and evaluation data to the GPU in this form. g. I am following this It would be helpful to extend the train method of the Trainer class with additional parameters to specify the GPUs devices we want to use during training. While it is advised to max out GPU usage as much as possible, a high number of gradient accumulation steps can result in a more pronounced training slowdown. It looks like the default fault setting local_rank=-1 will turn off distributed training However, I’m a bit confused on their latest version of the code If local_rank =-1 , then I imagine that n_gpu would be one, but its being set to torch. py to use torch. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional Feature request. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for Hi, I want to train Trainer scripts on single-node, multi-GPU setting. This concludes the introduction to fine-tuning using the Trainer API. It just puts everything on gpu:0, Trainer¶. I’m following the training framework in the official example to train the model. wnich ozaf phoocu zhpu pkg yzobqj dihr qdzwn amde bkys